SealFSv2: combining storage-based and ratcheting for tamper-evident logging
Tamper-evident logging is paramount for forensic audits and accountability subsystems. It is based on a forward integrity model: upon intrusion, the attacker is not able to counterfeit the logging data generated before controlling the system. There are local and distributed solutions to this problem. Distributed solutions are suitable for common scenarios, albeit not appropriate for autonomous and loosely connected systems. Moreover, they can be complex and introduce new security issues. Traditional local tamper-evident logging systems use cryptographic ratchets. In previous works, we presented SealFS (from now on, SealFSv1), a system that follows a radically different approach for local tamper-evident logging based on keystream storage. In this paper, we present a new version, SealFSv2, which combines ratcheting and storage-based log anti-tamper protection. This new approach is flexible and enables the user to decide between complete theoretical security (like in SealFSv1) and partial linear degradation (like in a classical ratchet scheme), exchanging storage for computation with user-defined parameters to balance security and resource usage. We also describe an implementation of this scheme. This implementation, which showcases our approach, is an optimized evolution of the original sealfs Linux kernel module. It implements a stackable file system that enables transparent tamper-evident logging to all user space applications and provides instant deployability. Last, we present a complete performance evaluation of our current implementation and a fair performance comparison of the two opposite approaches for local tamper-evident logging (i.e., storage-based vs. ratcheting). This comparison suggests that, on current systems and general-purpose hardware, the storage-based approach and hybrid schemes perform better than the traditional ratchet approach.
Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is partially funded under the Proyectos de Generación de Conocimiento 2021 call of Ministry of Science and Innovation of Spain co-funded by the European Union, project PID2021-126592OB-C22 CASCAR/DMARCE.
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